Skip to content
Analytica > Blogs > Renewable energy analysis and the bottom line

Renewable energy analysis and the bottom line

Sun, wind and biomass are the renewable energy sources that often come to mind, with geothermal, tidal and hydropower as further choices. The fear about fossil fuels running out is a driving force for the development of renewable energy sources, but not the only one: reduction of pollution and lower costs are also part of the equation. Lower costs? That needs a little further investigation. In the longer run, that should be the case. After all, the sources of renewable energy like the oceans are already in evidence and available – no drilling, no pipelines, and no tankers required. However, using starting to use renewable energy often requires a non-negligible investment. And making a renewable energy analysis of costs and uptake depends on more factors than whether or not tomorrow will be a fine day.

Triple bottom line model - people, planet, profit

Image source:

Government intervention in renewable energy

In several countries in the world, government subsidies are or have been available to encourage people to install solar panels either in their homes or their businesses. Grants have been made available for the construction of wind turbines and tidal motors; in some countries, notably the Nordics, state-controlled electricity providers have a clear policy of using hydro or geothermal power. Yet government incentives are sometimes taken away too. At the time of writing, Spain is preparing to cut renewable energy subsidies; the state was already paying renewable energy producers over the odds for their solar or wind generated energy. In Washington DC, the House already voted to cut the budget for renewable energy research.

Renewable energy analysis and the cost of accuracy

Image source:

A perfect energy solution in an imperfect market

Any renewable energy analysis must therefore take account of these variations in public sector policies. The market is not driven solely by a start-up cost/payback logic, but by additional elements that include ancillary financial or fiscal factors, notions of social responsibility and for businesses in particular pressure from stakeholders. Models to predict the popularity and use of renewable energy, such as the one for Tucson Electric Power using Analytica, must deal with a fluid situation in which decision factors may come and go. Forecasting the cost of compliance with state directives and the impact of its own incentives made the Intelligent Array algorithm of Analytica a key enabler in changing the model to accommodate varying scenarios or technology choices.

Time for some catastrophe theory?

If utilities companies and local or national government are to be able to predict whether or not renewable energy will catch on, they may need to model the tipping point (using math from the corresponding discipline of catastrophe theory). There is already substantial social pressure on organizations to use renewable energy. As more entities (hopefully) declare their intention or their use of renewable sources, the pressure on those who have not yet made the change may grow further. The situation is similar to the same way fashion items become hits, company stocks experience upswings, or electric cars find favor. Renewable energy analysis can then predict at which point ‘tipping’ would occur according to different scenarios of energy pricing and state intervention, and what the bottom line would be for those involved.

If you’d like to know how Analytica, the modeling software from Lumina, can help you with renewable energy analysis and other business modeling, then try Analytica’s free trial to see what it can do for you.

Share now